Multi-Objective Ergodic Search for Dynamic Information Maps

Robotic explorers are essential tools for gathering information about regions that are inaccessible to humans. For applications like planetary exploration or search and rescue, robots use prior knowledge about the area to guide their search. Ergodic search methods find trajectories that effectively balance exploring unknown regions and exploiting prior information. In many search based problems, the robot must take into account multiple factors such as scientific information gain, risk, and energy, and update its belief about these dynamic objectives as they evolve over time. However, existing ergodic search methods either consider multiple static objectives or consider a single dynamic objective, but not multiple dynamic objectives. We address this gap in existing methods by presenting an algorithm called Dynamic Multi-Objective Ergodic Search (D-MO-ES) that efficiently plans an ergodic trajectory on multiple changing objectives. Our experiments show that our method requires up to nine times less compute time than a naïve approach with comparable coverage of each objective.

Authors

• Ananya Rao
• Abigail Breitfeld
• Alberto Candela
• Benjamin Jensen
• David Wettergreen
• Howie Choset

Keywords

• Space vehicles
• Heuristic algorithms
• Computational modeling
• Pareto optimization
• Search problems
• Data models
• Trajectory
• Functional Dynamics
• Ergodic Search
• Computation Time
• Scientific Information
• Multiple Objects
• Dynamic Objects
• Dynamic Search
• Planetary Exploration
• Search Algorithm
• Weight Vector
• Objective Value
• Pareto Front
• Tree Search
• Change Objectives
• Static Function
• Map Points
• Multiple Mapping
• Static Objects
• Fourier Coefficients
• Update Function
• Map Objects
• Monte Carlo Tree Search
• Rapidly-exploring Random Tree
• Dynamic Update
• Space Robot
• Positive Ideal Solution
• Cuprite
• Pareto Optimal Set
• Trajectory Planning
• Negative Ideal Solution